Preliminary Investigation on Marine Radar Oil Spill Monitoring Method Using YOLO Model

Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.

Automated summary

Due to the rapid growth of ocean oil development and transportation, the risk of offshore oil spills has increased unevenly, posing a great threat to coastal cities. Therefore, an automatic oil spill detection method based on the YOLO deep learning network was proposed to minimize disaster losses. The detection model can effectively identify oil spill monitoring regions and extract oil slicks using an adaptive threshold. The proposed method provides real-time and effective data for routine patrols and emergency responses.